Department of
COMPUTER-SCIENCE






Syllabus for
Master of Science (Computer Science and Applications)
Academic Year  (2020)

 
1 Semester - 2020 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCSA131 PROGRAMMING IN JAVA 4 4 100
MCSA132 DIGITAL LOGIC AND COMPUTER ORGANISATION 4 04 100
MCSA133 ADVANCED DATABASE MANAGEMENT SYSTEM 4 4 100
MCSA134 DATA ANALYTICS 4 4 100
MCSA151 PROGRAMMING LAB - I 4 02 100
2 Semester - 2020 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCSA231 DATA STRUCTURES AND ALGORITHMS 4 04 100
MCSA232 DATA COMMUNICATION AND NETWORK SECURITY 4 4 100
MCSA233 ADVANCED OPERATING SYSTEM 4 4 100
MCSA234 BUSINESS INTELLIGENCE 4 04 100
MCSA251 PROGRAMMING LAB - II 4 02 100
3 Semester - 2019 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCSA331 COMPUTER GRAPHICS 4 4 100
MCSA332 WEB ENGINEERING 4 04 100
MCSA341B IOT AND WIRELESS SENSOR NETWORKS 4 4 100
MCSA342D DATA MINING AND DATA WAREHOUSING 4 04 100
MCSA381 SPECIALIZATION PROJECT 4 2 100
MCSA382 RESEARCH 2 1 50
4 Semester - 2019 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCSA431 CLOUD COMPUTING 4 4 100
MCSA441A SOFTWARE PROJECT MANAGEMENT 4 04 100
MCSA441B SOFTWARE ARCHITECTURE 4 4 100
MCSA441C SOFTWARE QUALITY AND TESTING 4 4 100
MCSA441D OOAD WITH UML 4 04 100
MCSA441E PRINCIPLES OF USER INTERFACE DESIGN 4 04 100
MCSA441F RISK ANALYSIS 4 04 100
MCSA442A MACHINE LEARNING 4 4 100
MCSA442B NEURAL NETWORKS 4 4 100
MCSA442C DIGITAL IMAGE PROCESSING 4 4 100
MCSA442D COMPUTER VISION 4 4 100
MCSA442E AGENT BASED COMPUTING 4 4 100
MCSA442F EVOLUTIONARY COMPUTING 4 4 100
MCSA481 MAIN PROJECT 2 4 200
MCSA482 RESEARCH (IMPLEMENTATION AND PUBLICATION) 2 1 50
        

  

Assesment Pattern

Question paper has to be set for the total marks of 100.

Examination duration is 3 hours.

Each full question carries 10 marks.

Answer any 10 questions out of 14.

Examination And Assesments

Evaluation Pattern: 60% CIA + 40% ESE 2. Tutorials / Assignments / Tests / Quiz / Seminar. 3. Attendance is part of the CIA component. 4. Minimum percentage to pass in each paper is 50% (CIA + ESE).

Department Overview:
Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.
Mission Statement:
Vision The Department of Computer Science endeavors to imbibe the vision of the University ?Excellence and Service?. The department is committed to this philosophy which pervades every aspect and functioning of the department. Mission ?To develop IT professionals with ethical and human values?. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the students t
Introduction to Program:
MSc programme is offered by the University for the professionals working in the software industry or related fields. This program is intended to enhance their existing academic foundations with comprehensive understanding of the use and application of information technology. The programme focuses on Advanced Operating Systems, Data Structures, Software Project Management, Networks, Data Warehousing and Data Mining.
Program Objective:
Programme Objective ? The program is designed to help software professionals who are already employed to further their knowledge in their respective domains. ? To enhance the project management skills. ? To facilitate software professionals to take lead roles. ? To understand and assimilate knowledge and skills to apply in their industry. ? To introduce contemporary theoretical concepts about the processes, standards and practices in software development life cycle. ? To assist in career advancement by acquiring additional degree.

MCSA131 - PROGRAMMING IN JAVA (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To introduce the concepts and principles of Java Programming language and to design and implement object-oriented solutions to simple and complex problems. To experience in java programming within an integrated development environment.

Learning Outcome

Upon successful completion of the course, the student will be able to

CO 1: Recognize the principles and practice of object-oriented programming in the construction of robust maintainable programs.

CO 2: Show competence in the use of Java Programming language in the development of small to medium-sized application programs that demonstrate professionally acceptable coding and performance standards.

CO 3: Design real-time applications in various domains.

Unit-1
Teaching Hours:12
Fundamentals of Java Programming
 

Review of the fundamentals of Java Programming - Class and Objects - Inheritance in Java - Inheritance in classes - Using super - Method overriding - Dynamic Method  Dispatch  -  Abstract Classes - Using final with inheritance - the Object Class - Interfaces and Packages - Inheritance in java with Interfaces - Defining Interfaces - Implementing Interfaces - Extending Interfaces - Creating Packages - CLASSPATH variable - Access protection - Importing Packages- Interfaces in a Package - Exception Handling in Java - try-catch-finally mechanism - throw statement - throws statement - Classes for Exception Handling

 

Unit-2
Teaching Hours:12
Input / Output in java, Multi threading, Applets
 

Input / Output in java - java.io package - I/O Streams - Readers and Writers - Using various I/O classes – Reader, Writer, Input Stream and Output Stream - Serialization of objects Multithreading - Life cycle of a thread - Java Thread priorities - Runnable interface and Thread Class - Sharing limited Resources - Shared Object with Synchronization – Comparators – Collections - Collection-classes – List – Set – Maps – Trees - Iterators

Unit-3
Teaching Hours:12
GUI Components (awt& swing) , Swing, Servlets
 

GUI concepts in java - Basic GUI Components in AWT - Container  Classes -  Layout  Managers - Flow Layout - Border Layout-Card Layout - Box Layout - Difference between AWT and SWING - Event Handling-Handling Keyboard Events and Mouse Events - Handling Sessions and Cookies - Servlet Model – Overview - Environment Setup - Life Cycle  -  Examples - Client Request - Server Response

 

Unit-4
Teaching Hours:12
Database and client server communication
 

Networking - Creating a server that sends data - Creating a client that receives data - two way communications between server and client - Difference between Server Socket and Socket – RMI - JDBC - Using MS-Sql Server Stages in a JDBC program - Registering the driver - Connecting to database - Transaction and Non-Transactional Events - Preparing SQL statements

- various methods of statements and differences - Improving the performance of a JDBC program

 

Unit-5
Teaching Hours:12
JSP Basics, Directive Elements, Custom Tags
 

Java Server Pages - The Problem with Servlets - Life Cycle of JSP Page - JSP Processing - JSP Application Design with MVC - Setting Up the JSP - Environment - JSP Directives - JSP Action- JSP Implicit Objects - JSP Form Processing - JSP Session and Cookies Handling - JSP Session. Tracking - JSP Database Access - JSP Standard Tag Libraries - JSP Custom Tag - JSP Expression Language - JSP Exception Handling - JSP XML Processing

Text Books And Reference Books:

[1].    Schildt Herbert, “The Complete Reference”, Java Eighth Edition, Tata McGraw-Hill, 2011

[2].    Kathy Walrath,“ Java server programming J2EE”, 1st ed., Black Book, Dream Tech Publishers, 2015.

Essential Reading / Recommended Reading

[1].    Deitel&Deitel, “Java How to Program”, Pearson Education Asia, 10th Edition, 2015.

[2].    RaoNageswara, “Core Java: An Integrated Approach”, Dreamtech Press, 2nd Edition, 2010.

[3].    James Keogh, “Complete Reference J2EE” McGraw publication, 2015.

 

Evaluation Pattern

CIA : 60%

ESE : 40%

MCSA132 - DIGITAL LOGIC AND COMPUTER ORGANISATION (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

Course Objectives:

To enable the students to learn the basic functions, principles and fundamental aspects of computer architecture and design in terms of digital logic elements and circuits, central processing unit and memory unit.

 

 

Learning Outcome

CO1:  Understand different number system, binary codes and digital logic elements

CO2: Acquaint with elementary postulates of Boolean algebra and methods for simplifying Boolean expressions

CO3Illustrate the procedures for the analysis and design of sequential and combinational circuits

CO4: Demonstrate the basic structure and operation of processing unit and get familiarize with different types of memory systems

 

 

Unit-1
Teaching Hours:12
Number System and Binary Coding
 

Number system- Decimal number system- Binary number system- octal number system- hexadecimal number system- number system conversion- number representation- unsigned representation – signed number representation-1’s complement – 2’s complement- 9’s complement – 10’s complement- binary arithmetic operation- binary addition- binary subtraction- Binary multiplication- binary division- Binary codes- BCD and Gray code

Digital Logic Elements

Introduction- Boolean algebra- Boolean operators- truth table- laws of Boolean algebra- De Morgan’s Law- Logic gates- Description of logic gates- Universal properties- Simplification of logic functions- Realization using NAND and NOR  gate

Self learning: Implementation using simulator

Unit-2
Teaching Hours:12
Combinational circuits
 

                                                                                                          

Logic expression- minterm - maxterm- SOP - POS expression- minimization techniques- Karnaugh Map

Combinational circuits- Half Adder – Full adder- Half subtractor-Full subtractor- Binary adder-Binary subtractor-Binary adder subtractor-BCD adder – Realization using NAND gates -Binary multiplier- Encoder- Decoder- Multiplexer- Demultiplexer-BCD to seven segment display

 

Unit-3
Teaching Hours:12
Sequential Circuits (Flip Flops with Timing Diagram)
 

Sequential Circuit Definitions - Latches- Clock - Types of Clock – positive - Negative edge triggered -  Flip-Flops- SR Flip Flop – D Flip Flop – JK Flip Flop -Edge Triggered Flip Flop- T Flip-Flop - Master-Slave JK Flip-Flop-Timing diagram.

Unit-4
Teaching Hours:12
Registers and Counters
 

                                                                               

Definition of Register and Counter – Registers - Shift Registers – Serial Transfer –  Modes of operations-SISO-SIPO –PISO-PIPO- Shift register with Parallel Load and Bidirectional Shift Register - Synchronous Counter -  Asynchronous Counters -  Binary Counters -  Up/Down counter -BCD counter.

Unit-5
Teaching Hours:12
Computer Organization
 

                                                                                

 

Basic Structure of Computers: Basic Operational Concepts- Bus Structures – Processor Clock - Clock Rate- Instruction set: CICS and RISC.

Basic Processing Unit: Fundamental Concepts, Multiple Bus Organization, ALU, Von-Neumann architecture.

The memory system: RAM (Static and Dynamic)-ROM-PROM-EPROM-Cache Memory

Text Books And Reference Books:

[1] Donald P Leach, Albert Paul Malvino, Goutam Saha, Digital Principles and Applications, 8th Edition, Tata Mc Graw-Hill, 2018

[2]. William Stallings Computer Organisation and Architecture, 10th edition, Pearson,2016

 

Essential Reading / Recommended Reading

[1] Mano, Morris M and Kime Charles R.Logic and Computer Design Fundamentals, Pearson education, 2nd edition, 2015.

[2] Bartee, Thomas C, Digital Computer Fundamentals, Tata Mc Graw-Hill, 6th edition, 2016.

[3] William Stallings, Computer Architecture and Organization, PHI, Eigth  Edition, 2016.

[4] David A. Patterson and John L.Hennessey, Computer Organization and Design, Morgan Kauffman / Elsevier, Fifth edition, 2016.

 

 

 

Evaluation Pattern

CIA - 60%

ESE - 40%

MCSA133 - ADVANCED DATABASE MANAGEMENT SYSTEM (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course concentrates on the introduction, principles, design, and implementation of advanced database concepts. The objective of the course is to provide a strong foundation of database concepts and develop skills for the design, storage, and retrieval in relational  databases,  XML and NoSQL databases.

Learning Outcome

Upon successful completion of the course, the student will be able to

CO 1: Understand the fundamental and advanced concepts of relational databases.

CO 2: Demonstrate storage and retrieval in XML and NoSQL.

CO 3: Design Database Application using CRUD operations.

Unit-1
Teaching Hours:12
Introduction to Relational Databases
 

Database system applications - Purpose of database systems - View of data - Data models - Database languages - Database storage and querying - Transaction management - Database architecture - Database users and administrators

Unit-2
Teaching Hours:12
ER Model and Relational Database Design
 

Structure of relational databases - Database schema - Keys, Schema diagrams, Design process - ER model – Constraints - ER diagrams - Aspects of database design - Atomic domains and 1NF - Decomposition using functional dependencies - Functional dependency theory

Unit-3
Teaching Hours:12
Database Storage and Indexing
 

File organization - Organization of records in files - Data dictionary storage - Basic indexing concepts - Ordered indexes - B+ tree index - Static hashing - Dynamic hashing - Bitmap index

Unit-4
Teaching Hours:12
XML Data Model
 

Motivation - Structure of XML Data - XML Document Schema - Querying and Transformation - Application Program Interfaces to XML - Storage of XML Data - XML Applications.

Unit-5
Teaching Hours:12
NoSQL
 

Definition and introduction - Document databases – MongoDB - Storing data and accessing data from MongoDB - Querying MongoDB - Document store internals - MongoDB reliability and durability - Horizontal scaling - CRUD operations in MongoDB - Creating and using indexes in MongoDB

Text Books And Reference Books:

[1]. Abraham Silberschatz, Henry Korth, Sudarshan, “Database System Concepts”, McGraw- Hill, 6th Edition, 2011.

[2]. Shashank Tiwari, “Professional NoSQL”, John-Wiley, 2011.

Essential Reading / Recommended Reading

[1]. Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”, McGraw-Hill, 3rd Edition, 2014.

[2]. RamezElmasri, ShamkantNavathe, “Fundamentals of Database Systems”,  Addison- Wesley, 6th Edition, 2011.

[3]. Kristina Chodorow, “MongoDB: The Definitive Guide”, O'Reilly, 2nd Edition, 2013.

Evaluation Pattern

CIA : 60%

ESE : 40%

MCSA134 - DATA ANALYTICS (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Data Science is the latest buzz word in the modern era of cloud and big data in academic research and corporate world. Data Science experts must acquire analytical skill set for pursuing research and generating new knowledge in the business process. Data Analytics course delivers various techniques to discover new and hidden knowledge from the data set. This course provides insight into the complete research process in phases as research methodology, data exploration, modeling, evaluation and visualization. R programming, Python programming, MATLAB and Excel are the suggestive tools for implementation.

Learning Outcome

Upon successful completion of the course the student will be able to:

CO1: Collect data from various sources

CO2: Explore data using tools

CO3: Build analytical models

CO4: Interpret results based on the choice of domain

Unit-1
Teaching Hours:12
Introduction and Data Exploration
 

Introduction - Data and Relations - Matrix representation - variable measures - sequential relation - sampling and qquantization. Data Pre-processing - Cleaning - Transformation - Basic Visualization - PCA - multidimensional scaling - Histogram - Correlation.

Unit-2
Teaching Hours:12
Predictive Modeling and Optimization
 

Linear and non-linear regression - Feature Selection - Forecasting - Recurrent Models - Classification - Rules - Trees - Naive Bayes - SVM - Vector Quantization - Evaluation Metrics - Validation and Interpretation.

Unit-3
Teaching Hours:12
Optimization and Clustering
 

Optimization Methods - With derivatives - Gradient Descent - Clustering - Cluster Partition - Sequential - Prototype-Based - Relational - Cluster Validity and Self Organizing map.

Unit-4
Teaching Hours:12
Mathematical Modeling and Spatial Data
 

Introduction to Multi-criteria Decision Making - Using Numerical Methods in Data Science - Mathematical Modeling with Markov Chains - Modeling Spatial Data with Statistics - Getting predictive surfaces from special point data - Usig trend surface analysis on spatial data.

Unit-5
Teaching Hours:12
Visualization
 

Principles of Visualization - Understanding the type - Design Style - Data Graphic Type - Web-based Applications for Visualization Design - Best practices in dashboards - Making maps for Spatial Data.

Sef Learning: Additional Exploration and Modeling Algorithms

Service based learning: Building models for social relevance issues

Text Books And Reference Books:
  1. Runkler, Thomas. A, "Data Analytics: Models and Algorithms for Intelligent Data Analysis", Springer, 2012.
  2. Lilean Pearson, "Data Science For Dummies", John Wiley and Sons, 2015
Essential Reading / Recommended Reading
  1. Jain P and Sharma P, "Behind Every Good Decision: How Anyone Can Use Business Analytics To Turn Data into Profitable Insight", Amacom, 2014.
  2. John W Foreman, "Data Smart: Using Data Science to Transform Information into Insight", Wiley, 2013.
Evaluation Pattern

CIA - 60%

ESE - 40%

MCSA151 - PROGRAMMING LAB - I (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:02

Course Objectives/Course Description

 

The course builds the logical thinking in the students with the help of the programming. It also facilitates the students to build applications using java programming. The database concepts help the student to learn advance database connectivity and usage.

Learning Outcome

Upon the completion of the course, the student will be able to

CO1: Demonstrate the skills for identifying logic in the problem

CO2: Analyze the given problem and write the algorithm, flowchart

CO3: Write structured java programs and implement the advance database concepts

Unit-1
Teaching Hours:60
Java Programming
 

1. Demonstrate objects and classes (constructor, access specifier, method overloading)

2. Demonstrate static block, static variables and static methods

3. Demonstrate inheritance in java

4. Demonstrate use of super and this

5. Demonstrate abstract class

6. Demonstrate interfaces in java

7. Demonstrate exception handling in java

8. Demonstrate multithreading in java

9. Demonstrate applets in java

10. Demonstrate two way communication between server and client

Unit-1
Teaching Hours:60
Advanced Database Management System
 

1. Select queries and DML

2. PL/SQL

3. Data manipulation with MongoDB

Text Books And Reference Books:

[1] Schildt Herbert, Java Eighth Edition: The Complete Reference, Tata McGraw-Hill, 2011

[2] Black Book “ Java server programming” J2EE, 1st ed., Dream Tech Publishers, Kathywalrath, 2015.

Essential Reading / Recommended Reading

[1] Deitel & Deitel, Java How to Program, Pearson Education Asia, 10th Edition, 2015.

[2] Rao Nageswara, Core Java: An Integrated Approach, Dreamtech press, 2nd Edition, 2010.

 [3] Complete Reference J2EE by James Keogh mcgraw publication, 2015.

Evaluation Pattern

Evaluation Pattern: 60% CIA + 40% ESE

MCSA231 - DATA STRUCTURES AND ALGORITHMS (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

This course provides a more comprehensive understanding of data structure and algorithm development with practical implantation of the concepts.

Learning Outcome

Upon completion of the course, student will be able to

CO 1: Understand the need and working of advanced search and sorting technique.

CO 2: Calculate and measure efficiency of algorithm.

CO 3: Appreciate some interesting algorithms like Huffman, Quick Sort, and Shortest Path etc.

Unit-1
Teaching Hours:12
Stacks and Queues
 

Basic Operations – Implementation - Stack applications - Recursion- A Case Study - Queue operations – implementation - Applications of a queue - Linked Lists: Linked list algorithms - Processing a linked list - Linked list algorithms (Create, Traverse, Insert, Delete, Search, Destroy) - doubly linked lists structures and its operations - Applications of linked lists - Algorithmic efficiency in terms of space and timecomplexity

Unit-2
Teaching Hours:12
Trees
 

Basic tree concepts - Binary trees - Binary tree traversals - Expression trees - General trees - Changing general tree to binary tree - General tree insertion - Search trees - Binary search trees – Operations – Traversals : BFS and DFS methods - Searching a BST - Algorithms for and traversing and searching - AVL trees - AVL Balance factor - Balancing trees - AVL Insert - AVLDelete

Unit-3
Teaching Hours:12
Multiway trees
 

M-Way search trees - B Trees - B-Insertion - B-Tree Deletion - B –Tree Traversal - Tree Search

- Simplified B-Trees - 2-3 Tree - 2-3-4 Tree - B-Tree Variations – B * Trees - B+ Trees.

Unit-4
Teaching Hours:12
Graphs
 

Terminology – operations - Graph storage structures – Adjacency Matrix - Adjacency lists - Graph algorithms - Create insert vertex - Delete vertex - Retrieve vertex - Depth first traversal and Breadth First Traversal – Networks : Minimum spanning tree - Shortest Pathalgorithm

Unit-5
Teaching Hours:12
Advanced Sorting & Searching concepts
 

General sort concepts - O Notation - Sort Algorithms - Quick Sort - Heap sort - Sorting using a Heap - Shell sort - Merge sort - radix sort - merging two sorted lists - Efficiency considerations - comparative study

Text Books And Reference Books:

[1]. Richard F. Gilberg, Behrouz A. Forouzan, “Data Structure. A Pseudocode Approach with C”, 3rd Edition, Thomson Publications, reprint 2006.

[2]. A M Tanenbaum, Y Langsam and M. J. Augenstein, “Data Structure using C”, 2nd Edition, Prentice- Hall, India, 2007.

Essential Reading / Recommended Reading

[1]. Robert Kruse, Tondo C L, Bruce Leung, Data Structures & program Design In C, Pearson Education, 2nd Edition, 2004.

[2]. U.A.Deshpande and O. G. Kakde, Data Structures and Algorithms, ISTE- learning.

Evaluation Pattern

1.      Evaluation Pattern: 60% CIA + 40% ESE

2.      Tutorials / Assignments / Tests / Quiz / Seminar.

3.      Attendance is part of the CIA component.

4.      Minimum percentage to pass in each paper is 50% (CIA + ESE).

MCSA232 - DATA COMMUNICATION AND NETWORK SECURITY (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course aims to set the foundation for Data Communication and Network Security by introducing the network components, topologies, network models, layers, protocols, and some of the security aspect.

Learning Outcome

Upon successful completion of the course the students will be able to

CO1: Comprehend knowledge about Network Architecture and its functionality. 

CO2: Evaluate network protocols for data transmission in various types of networks.

C03: Understand the working of Algorithm in Cryptography

CO4: Design solution to real time problems related to Network Security and compression. 

Unit-1
Teaching Hours:12
Data Communications
 

Data Communications- Data Transmission: Concepts and Terminology, Analog and Digital Data Transmission, Transmission Impairments, Channel Capacity; Transmission Media: Guided Transmission Media, Wireless Transmission, Wireless Propagation, Line-of-Sight Transmission; Signal Encoding Techniques: Digital Data, Digital Signals, Digital Data, Analog Signals, Analog Data, Digital Signals, Analog Data, Analog Signals.

Unit-2
Teaching Hours:12
Digital Data Communication
 

Digital Data Communication Techniques- Asynchronous and Synchronous Transmission, Types of Errors, Error Detection, Error Correction, Line Configurations; Multiplexing: Frequency, Division Multiplexing, Synchronous Time-Division Multiplexing, Statistical Time-Division Multiplexing, Asymmetric Digital Subscriber Line, Switched Communications Networks, Circuit Switching Networks, Circuit Switching Concepts, Softswitch Architecture, Packet-Switching Principles.

Unit-3
Teaching Hours:12
Congestion Control
 

Congestion Control in Data Networks- Effects of Congestion, Congestion Control, Traffic Management, Congestion Control in Packet-Switching Networks; Cellular Wireless Networks: Principles of Cellular Networks, First Generation Analog, Second Generation CDMA, Third Generation Systems; High-Speed LANs: The Emergence of High-Speed LANs, Ethernet, Fibre Channel; Wireless LANs: IEEE 802.11 Architecture and Services; Internetwork Protocols - Internetwork Protocols: Internet Protocol, IPv6; Transport Protocols: Connection-Oriented Transport Protocol Mechanisms, TCP, TCP Congestion Control, UDP.

Unit-4
Teaching Hours:12
Cryptography and Network Security
 

The need for security, Security Approaches, Security Attacks, Security Services, Security Mechanisms, A Model for Network Security. Symmetric Cipher Models, Substitution Techniques, Transposition Techniques and other Symmetric key approaches. Data Encryption Standard, AES Cipher. Public Key Cryptosystems, RSA Algorithm and Diffie-Hellman Key Exchange

Unit-5
Teaching Hours:12
Cryptographic Hash Function
 

Application of Cryptographic Hash Function, Brute Force Attack, Secure Hash Algorithm (SHA-2 & SHA-3), Message authentication code, HMAC, Digital Signatures (DSS). User Authentication: Kerberos Federated Identity Management. E-Mail Security, Pretty Good Privacy, S/MIME, SSL, IP Security Overview.

Text Books And Reference Books:

[1]     Stallings William, “Data and Computer Communications”, PHI, 9th Edition, 2011.

[2]     William Stallings, “Cryptography and Network Security”, Prentice Hall, 6th Edition, 2014.

Essential Reading / Recommended Reading

[1]      Forouzan, Behrouz A., “Data Communications and Networking”, Tata McGrawHill publishing Company Limited, 5th Edition, 2013.

[2]      AtulKahate, “Cryptography and Network Security”, Tata McGraw-Hills, 2010.

[3]      Brijendra Singh, “Network Security and Management”, PHI, 3rd Edition, 2013.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MCSA233 - ADVANCED OPERATING SYSTEM (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The course will expose few advanced topics in operating system and concepts related to recent developments in operating system. Objectives of the course are to understand the main concepts of parallel processing systems, distributed systems, real time systems etc., to have an insight into UNIX and MACH operating system and to know the components and management aspects of Real time, Mobile Operating Systems.

Learning Outcome

Upon successful completion of the course the students will be able to

CO 1: Analyse the requirements of Operating System.

CO 2:  Understand the concept of distributed operating system and concepts.

CO 3: Demonstrate the advanced OS concepts of Real time OS and Mobile OS.

Unit-1
Teaching Hours:12
Overview
 

General Overview of the System - System Structure – Operating System Services – Introduction to kernel - architecture of unix operating system - introduction to system concepts kernel data structures - The Buffer cache - Buffer Headers – Structure of the buffer pool – Retrieval of a buffer – scenarios for retrieval of a Buffer - Reading and writing disk blocks – Advantages and disadvantages of the buffer cache - Internal Representation of files – Inodes - structure of a regular file - directories - conversion of a path to an inode - Super Block - inode assignment to a New File - Allocation of Disk Blocks - Other file types

Unit-2
Teaching Hours:12
Process Management
 

UNIX Process Management - The Structure of Processes: Process States and Transitions - Layout of system memory - Context of a process – Sleep – Implementation of System Calls. Process Control - Process Creation – Signals – Process Termination – Invoking other programs – PID & PPID – Changing the size of a process – The shell – System Boot and the init process

Unit-3
Teaching Hours:12
Memory Management
 

Memory Management: Swapping – Demand Paging – A Hybrid System with Swapping and Demand Paging. The I/O Subsystem: Driver Interfaces – Disk Drivers – Terminal Drivers – Streams. Inter Process Communication (IPC): Process Tracing – System V IPC – Network Communications – Sockets. Multiprocessor Systems: Problem with Multiprocessor Systems – Master and Slave processors – Semaphores

Unit-4
Teaching Hours:12
Distributed System and RPC
 

Introduction to Distributed system – Remote Procedure Call – Logical clocks – Vector clocks – Distributed mutual exclusion – Non token based algorithms – Token based algorithms – Deadlock algorithms – Election algorithms – Byzantine agreement problem – Load distributing algorithms – Performance comparison. Distributed File system Design – an overview

Unit-5
Teaching Hours:12
Real time Systems
 

Real time and Mobile Operating Systems – Basic Model of Real Time Systems – Characteristics – Applications of Real Time Systems – Real time Task Scheduling –Handling resource sharing . Mobile Operating System – Micro Kernel Design – Case study MACH: Introduction to MACH - Process management in MACH-processes-thread scheduling – memory management in MACH- Virtual memory – memory sharing

Text Books And Reference Books:

[1] Maurice J Bach, “The Design of Unix Operating System”, Prentice Hall of India Pvt. Ltd., New Delhi, Reprint 2007.

[2] Andrew S Tanenbaum, “Distributed Operating Systems”, PHI, reprint 2006.

[3] Rajib Mall, “Real Time Systems: Theory and Practice”, Pearson Education, India, 2006.

Essential Reading / Recommended Reading

[1]. Stan-Kelly-Bootle, “Understanding Unix”, BPB Publications, New Delhi, reprint,2006.

[2]. Arnold Robbins, “UNIX in a Nutshell”, In a Nutshell series, 3rd Edition, reprint 2007.

[3]. George Coulouris, Jean Dollimore and Tim Indberg, “Distributed Systems Concepts and Design”, 3rd Edition, Pearson Education, 2002.

[4]. Pradeep K Sinha, “Distributed Operating Systems – Concepts and Design”, PHI, 2006.

Evaluation Pattern

1.      Evaluation Pattern: 60% CIA + 40% ESE

2.      Tutorials / Assignments / Tests / Quiz / Seminar.

3.      Attendance is part of the CIA component.

4.      Minimum percentage to pass in each paper is 50% (CIA + ESE).

MCSA234 - BUSINESS INTELLIGENCE (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications support the activities of decision support, query and reporting, online analytical processing (OLAP) and analysis.

Learning Outcome

 

CO1: Understand the Technical components of BI

O2: Analyze the process involved in BI

CO3: Visualize the data and Generate Reports using report builder and power pivot

Unit-1
Teaching Hours:12
Requirements, Realities and Architecture
 

Defining Business Requirements: Introduction, Uncovering Business Value, Prioritizing the Business Requirements. Designing the Business Process Dimensional Model: Concepts and Terminology, Additional Design Concepts and Techniques. The Toolset: Microsoft DW/BI Toolset, Architecture and Overview of the Toolset.

Unit-2
Teaching Hours:12
Building and Populating the Databases
 

Creating the Relational Data Warehouse: Getting started, completing the physical design, Define storage and create constraints and supporting objects.

Master Data Services: Managing Master Reference Data, Introducing SQL Server MDS, Creating a Simple Application.

Design and Develop the ETL System: Developing the ETL Plan, Introducing SQL Server Integration Services, Extracting Data, Cleaning and Confirming Data, Delivering Data for Presentation.

Unit-3
Teaching Hours:12
Analysis Services
 

Core Analysis Services OLAP Database: Overview, Design the OLAP structure-Planning, getting started, Data source view, Dimension design, Editing dimension, Editing Cube, Physical Design Consideration.

Unit-4
Teaching Hours:12
Developing the BI Applications
 

Building the BI Applications in Reporting Services: Overview, High Level Architecture for Reporting, System Design and Development Process, Building and Delivering Reports, Reporting Options.

Unit-5
Teaching Hours:12
BI using Excel
 

Power Pivot and Excel: Using Excel for Analysis and Reporting, Architecture, Creating and using Power Pivot Databases, Power pivot Monitoring and Management.

Case study: Any Two Applications (eg. Healthcare, Retail Industry)

Text Books And Reference Books:

[1]. Joy Mundy, Warren Thornthwaiteand  Ralph Kimball, “The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset”, John Wiley & Sons, 2nd edition, 2011.

Essential Reading / Recommended Reading

[1]. Gert H.N. Laursen and JesperThorlund , “Business Analytics for Managers: Taking Business Intelligence beyond Reporting Paperback” , 2013

[2]. Mike Biere,“Business Intelligence for the Enterprise” , second edition, 2009

Evaluation Pattern

   Evaluation Pattern: 60% CIA + 40% ESE

MCSA251 - PROGRAMMING LAB - II (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:02

Course Objectives/Course Description

 

This course provides different ways to solve business intelligence problems using various data structures and writing programs for these solutions

Learning Outcome

Upon the completion of the course. student will be able to

CO1: Understand the concepts of data structure

CO2: Implement basic data structures like arrays and linked lists

CO3: Demonstrate optimal approaches to solve sorting and graph problems

 

CO4: Analyse the process involved in Business Intelligence

Unit-1
Teaching Hours:60
Data Structures
 

1. Implementation of insertion, selection and merge sorting Methods

2. Implementation of stacks

3. Implementation of queues

4. Implementation of Linked list

5. Implementation of two way linked list

6. Implementation of circular linked List

7. Implementation of Binary search tree

8. Implementation of radix and heap sort

9. Implementation of DFS for graphs

10. Implementation of BFS for graphs

Unit-1
Teaching Hours:60
Business Intelligence
 

Pre-Lab: Software Installation and Configuration

1. Create a Relational Data bases

2. Design an ETL System using SSIS

3. Design a dimensional model using SSAS

4. Design reports using SSRS

5. Design a dashboard using power pivot

Text Books And Reference Books:

1. Richard F. Gilberg, Behrouz A. Forouzan, “Data Structure. A Pseudocode Approach with C”, 3rd Edition, Thomson Publications, reprint 2006.

 

2. Joy Mundy, Warren Thornthwaite and Ralph Kimball, “The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset”, John Wiley & Sons, 2nd edition, 2011.

Essential Reading / Recommended Reading

1. Robert Kruse, Tondo C L, Bruce Leung, Data Structures & program Design In C, Pearson Education, 2nd Edition, 2004.

2.Gert H. N. Laursen and Jesper Thorlund, “Business Analytics for Managers: Taking Business Intelligence beyond Reporting Paperback” , 2013

Evaluation Pattern

60%  CIA  + 40% ESE

MCSA331 - COMPUTER GRAPHICS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To familiarize the students with the concepts of computer graphics like line, circle drawing algorithms, transformations, clipping, projection, color models, curves. To make the students understand how to implement the computer graphics concepts using OpenGL.

Learning Outcome

Upon the completion of the course the student will be able to

CO 1: Understand the basic concepts of Computer Graphics.

CO 2: Apply geometric conversions on graphical objects.

        CO 3: Implement the Graphics concepts using OpenGL

Unit-1
Teaching Hours:12
Introduction to Computer Graphics
 

Applications, Overview of Graphics Systems – Video display devices - Raster-scan systems - Graphics software - Introduction to OpenGL - Graphics Output Primitives Coordinate Reference Frames - Two-Dimensional frame in OpenGL - Point Functions - Line Functions - Line-Drawing Algorithms – DDA – Bresenhams - Curve Functions - Midpoint Circle Algorithm - and Display- window reshape function

Self-Learn: Area filling - Display lists - Basic colors - Attribute functions

Unit-2
Teaching Hours:12
Geometric Transformations
 

Basic two-dimensional geometric transformations - Homogeneous Coordinates - Composite transformations - Geometric transformations in three-dimensional space – Translation - Rotation – scaling - composite three-dimensional transformations - OpenGL geometric transformation functions

Self-Learn: Reflection - shear

Unit-3
Teaching Hours:12
Illumination and Color Models
 

Light sources - Basic illumination models - transparent surfaces - OpenGL illumination functions

- Color Models - Standard primaries and chromaticity diagrams - RGB color model - HSV color model - OpenGL color functions

Self-Learn: Ray-tracing and Texture mapping

Unit-4
Teaching Hours:12
Viewing
 

Two-dimensional viewing pipeline - clipping window - Normalization and viewport transformations - 2D viewing functions - Clipping Algorithms – Line clipping – Cohen- Sutherland and Liang-Barsky Line clipping - polygon clipping – Sutherland-Hodgman algorithm

- Three-dimensional viewing concepts – Projections - Three-dimensional viewing pipeline - Projection transformation - Parallel and Perspective projection matrices - 3D viewing functions

Self-Learn: Other clipping algorithms - Text clipping and Projection derivations

Unit-5
Teaching Hours:12
Three-dimensional Object Representations
 

Spline representations - Cubic spline interpolation methods - Bezier curves and B-Spline curves- OpenGL approximation-Splinefunctions

Text Books And Reference Books:

[1].    D. Hearn, M. Pauline Baker, “Computer Graphics with OpenGL”. PHI, 3rd Edition,2011.

Essential Reading / Recommended Reading

[1]. Foley, Vandam&Feiner, Hughes, “Computer Graphics Principles  &Practice,in  C”, Pearson Education (Singapore Pvt Ltd, Indian Branch, Delhi), 6th Indian Reprint2001.

[2]. Richard S Wright and Jr. Michael Sweet, “Open GL Super Bible:Comprehensive Tutorial and Reference”, 7nd Edition, Pearson Education, 2015.

Evaluation Pattern

60% CIA + 40%ESE

MCSA332 - WEB ENGINEERING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

The World Wide Web has become a major delivery platform for information resources.  Many applications continue to be developed in an ad-hoc way, contributing to problems of usability, maintainability, quality and reliability. This course examines systematic, disciplined and quantifiable approaches to developing of high-quality, reliable and usable web applications.

Learning Outcome

CO1: Understand the concepts, principles and methods of Web engineering.

CO2: Apply the concepts, principles, and methods of Web engineering to Web applications  development.

CO3: Correlate with current web technologies.

 

CO4: Collaborate with web application development software tools and environments currently available  on the market.

Unit-1
Teaching Hours:12
Requirements Engineering and Modeling
 

RE Fundamentals and Specifics - Principles for RE - Adapting RE Methods - Modeling Fundamentals and Specifics - Modeling Requirements - Content Modeling - Hypertext Modeling - Presentation Modeling - Customization Modeling

Unit-2
Teaching Hours:12
Web Application Architectures and Design
 

Fundamentals and Specifics – Components - Layered Architectures - Data-aspect Architectures - Evolutionary Perspective - Presentation Design - Interaction Design - Functional Design – Outlook

Unit-3
Teaching Hours:12
Testing, Operation and Maintenance
 

Fundamentals and Specifics of Testing - Test Approaches and Schemes - Test Methods and Techniques - Test Automation – Challenges in Launch of a Web Application - Promoting a Web Application - Content Management - Usage Analysis

Unit-4
Teaching Hours:12
Performance and Security
 

Characteristics of Performance - Definition and Indicators – Workload - Analytical Techniques - Representing and Interpreting Results - Performance Optimization Methods - Aspects of Security - Encryption, Digital Signatures and Certificates - Secure Client/Server-Interaction - Client Security Issues - Service Provider Security Issues

Unit-5
Teaching Hours:12
Technologies for Web Applications and Semantic Web
 

Fundamentals - Client/Server Communication - Client-side Technologies  - Ajax - Document-specific Technologies - Server-side Technologies - Fundamentals and Specifics of Semantic Web - Technological Concepts and Tools

Text Books And Reference Books:

[1].      GertiKappel and and Birgit Proll, “Web Engineering: The Discipline of Systematic Development of Web Applications”, John Wiley & Sons, 2012.

Essential Reading / Recommended Reading

[1].      Diane Cerra ,“Unleashing Web 2.0: From Concepts to Creativity”, Elsevier, 2010.

Evaluation Pattern

CIA: 60%

ESE: 40%

MCSA341B - IOT AND WIRELESS SENSOR NETWORKS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The explosive growth of the “Internet of Things” is changing our world and the rapid growth of IoT components is allowing people to innovate new designs and products at home. Wireless Sensor Networks form the basis of the Internet of Things. To latch on to the applications in the field of IoT of the recent times, this course provides a deeper understanding of the underlying concepts of IoT and Wireless Sensor Networks.

Learning Outcome

Upon the completion of the course the student will be able to

CO 1: Identify different issues in wireless ad hoc and sensor networks.

CO 2: Develop an understanding of sensor network architectures from a design and performance perspective.

CO 3: Understand the layered approach in sensor networks and WSN protocols.

CO 4: Implement real time IoT applications to create an impact

 

Unit-1
Teaching Hours:12
Introduction to IOT
 

Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols, Logical Design- Functional Blocks, Communication Models- Communication APIs- Introduction to measure the physical quantities, IoT Enabling Technologies - Wireless Sensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and Deployment Templates

Unit-2
Teaching Hours:12
IOT Programming
 

Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards (Rasberry Pi, Arduino) and IDE - Case Study: Weather Monitoring- Logical Design using Python, Data types & Data Structures- Control Flow, Functions- Modules- Packages, File Handling - Date/Time Operations, Classes- Python Packages of Interest for IoT.

Unit-3
Teaching Hours:12
IOT Applications
 

Home Automation – Smart Cities- Environment, Energy- Retail, Logistics- Agriculture, Industry- Health and Lifestyle- IoT andM2M.

Unit-4
Teaching Hours:12
Motivation for A Network Of Wireless Sensor Nodes
 

Sensing and Sensors, Wireless Sensor Networks, Challenges and Constraints; Applications: Structural Health Monitoring, Traffic Control, Health Care; Node Architecture, Operating system

Unit-5
Teaching Hours:12
MAC, Routing and Transport Control in WSN
 

Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC Case Study – Routing Challenges and Design Issues – Routing Strategies – Transport Control Protocols – Transport Protocol Design Issues – Performance of Transport Protocols

 

Text Books And Reference Books:

[1]. Arshdeep Bahga,Vijay Madisetti, “Internet of Things: Hands-on Approach”, Hyderabad University Press, 2015. (Unit -I to III)

[2]. Kazem Sohraby, Daniel Minoli, Taieb Znati ,“Wireless Sensor Networks: Technology.

Protocols and Application”, Wiley Publications, 2010 (Unit IV & V)

[3].    Waltenegus Dargie, Christian Poellabauer, "Fundamentals of Wireless Sensor Networks: Theory and Practice", A John Wiley and Sons, Ltd., Publication, 2010.

 

Essential Reading / Recommended Reading

[1]. Edgar Callaway , “Wireless Sensor Networks: Architecture and Protocols” , Auerbach Publications, 2003

[2]. Michael Miller, “The Internet of Things” , Pearson Education, 2015

[3]. Holger Karl, Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, John Wiley & Sons, Inc., 2005.

[4]. Erda lÇayırcı ,Chunming Rong, “Security in Wireless Ad Hoc and Sensor Networks”, John Wiley and Sons, 2009.

[5]. Carlos De Morais Cordeiro, Dharma Prakash Agrawal, “Ad Hoc and Sensor Networks: Theory and Applications (2nd Edition)”, World Scientific Publishing, 2011.

 

[6]. Adrian Perrig, J. D. Tygar, "Secure Broadcast Communication: In Wired and Wireless Networks", Springer, 2006.

Evaluation Pattern

CIA: 60%

ESE: 40%

MCSA342D - DATA MINING AND DATA WAREHOUSING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

The main objective of the course is designed to introduce the core concepts of data mining and data warehousing techniques and implementation

Learning Outcome

CO1: Demonstrating basic data mining algorithms, methods, and tools.

CO2: Design methods to mine data based on data mining principles and techniques. 

CO3: Building the Data warehouse.

CO4: Understanding the basic concepts of OLAP.

Unit-1
Teaching Hours:12
Introduction to Data Warehouse and OLAP
 

Basic elements of the Data Warehouse: Source system-Data staging Area-Presentation Server-Dimensional Model-Business process-Data Mart-Data warehouse-Operational Data Store-OLAP: ROLAP, MOLAP and HOLAP. DataWarehouse Design:The case for dimensional modeling – Putting Dimensional modeling together: the data warehouse bus architecture – Basic dimensional modeling techniques.

Unit-2
Teaching Hours:12
Data Warehouse Architecture
 

The value of architecture – An architectural framework and approach – Technical architecture overview – Back room data stores – Back room services. Back Room Services. Data Staging:Data staging overview – Plan effectively – Dimension Table staging – Fact Table loads and warehouse operations – Data quality and cleansing – issues.

Unit-3
Teaching Hours:12
Introduction to data Mining
 

Data Mining – Process and architecture - Kinds of Data to be mined - Data Mining Functionalities, Classification of Data Mining Systems, Data Mining Task Primitives, Major Issues in Data Mining. Data Preprocessing:Preprocessing - Descriptive Data Summarization – Measuring the central tendency- Measuring the dispersion of data - Data Cleaning - Missing Values – Noisy Data - Data Cleaning as a Process - Data Integration and Transformation - Data Reduction-Data Cube Aggregation-Attribute Subset Selection. Demo: Preprocessing can be done using WEKA tool.

Unit-4
Teaching Hours:12
Data Mining Algorithms
 

Association Rule Mining: Basic Concepts, Efficient and Scalable Frequent Item set Mining Methods – Apriori algorithm, Generating Rules – Improving efficiency – Mining frequent item set without candidate generation. Classification and Prediction: Issues Regarding Classification and Prediction, Accuracy and Error Measures.Cluster Analysis:Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods – K-Means and K-Medoids, Hierarchical Methods  Agglomerative and Divisive

Demo: Classification and clustering analysis can be done using WEKA tool.

Unit-5
Teaching Hours:12
Mining Time-Series and Spatial Data
 

Mining Time-Series Data – Trend analysis – Similarity search, Spatial Data Mining-Spatial Data Cube Construction and Spatial OLAP- Mining Spatial Association and Co-location Patterns-Spatial Clustering, Classification Methods-Mining Raster Databases. Applications and Trends in Data Mining: Data Mining Applications, Data Mining System Products and Research Prototypes, Social Impacts of Data Mining.

Text Books And Reference Books:

[1].      Kimball, Ralph, “The Data Warehouse Lifecycle Toolkit”, John Wiley & Sons, 2006.       

[2].    Jiawei Han and MichelineKamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, San Francisco, USA, 2nd Edition, 2011.

Essential Reading / Recommended Reading

[1].      Inmon W H, “Building the Data Warehouse”, John Wiley & Sons, 3rd Edition, 2005.

[2].      Margaret H. Dunham, “Data mining-Introductory and Advanced topics”, Pearson       Education, 2003.

[3].      Witten and E. Frank, “Data Mining : Practical Machine Learning Tools and        Techniques”, Morgan Kaufmann Publishers, 2005.

[4].      K P Soman, Shyam Diwakar, V. Ajay,“Insight into Data Mining-Theory and  Practice”, 6thReprint, PHI, 2012.

Evaluation Pattern

CIA:  60%

ESE:  40%

MCSA381 - SPECIALIZATION PROJECT (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

The course is designed to provide a real-world project development and deployment environment for the students.

Learning Outcome

CO1: Identify the problem and relevant modules for the selected problem.

CO2: Apply appropriate design/development methodology and tools.

CO3: Develop competency to work in a team and provide solutions as a product.

 

Unit-1
Teaching Hours:60
Specialization Project Lab
 

 

Project will be based on the specialization papers which students are opted for.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

60%

40%

 

 

 

MCSA382 - RESEARCH (2019 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

Course Description :

Evaluation Scheme and Rubrics

There is only CIA for this paper. Research work carried out in this semester is divided in two parts.

Part A constitutes data collection and pre-processing in which students should carry out the following tasks and submit the document for the same before the MSE.

  • Literature survey of existing data sets or any primary data sets in the respective area.

  • Gather the datasets from various sources (like visiting websites, universities, person, creating individually, etc.). Steps in pre-processing.

 

Part B constitutes modelling and implementation of their research work. Students should perform the following tasks:

  • Methodology. Evaluation and Discussion of Results.

  • Limitations, Conclusions and Scope for future enhancements. Plagiarism report.

Learning Outcome

Course Outcomes:

  • Able to produce valuable intellectual property.

  •  

  • Able to produce new products/processes/methods/model/Framework.

Unit-1
Teaching Hours:30
RESEARCH (RESEARCH PROBLEM IDENTIFICATION AND DATA COLLECTION)
 

Week 1 - Discussion and Identification of Research Domain (Updations)

Week 2 - Identification of Research Gap / OBJECTIVES OF RESEARCH

Week 3 - Research Design Phase - I

Week 4 - Research Design Phase - II

Week 5 - Research Design Phase - III

Week 6 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 7 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 8 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 9 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 10 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 11 - Implementation Phase - I

Week 12 - Implementation Phase - I (a)

Week 13 - Implementation Phase - I (b)

Week 14 - Implementation Phase - I (c)

Week 15 - Implementation Phase - I (d)

Text Books And Reference Books:

RESEARCH

Essential Reading / Recommended Reading

RESEARCH

Evaluation Pattern

CIA: 60%

ESE: 40%

MCSA431 - CLOUD COMPUTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Cloud computing has become a great solution for providing a flexible, on-demand, and dynamically scalable computing infrastructure for many applications. Cloud computing presents a significant technology trend. The course aims at familiarizing with the basic concepts of cloud computing and itsapplications.

Learning Outcome

Upon successful completion of the course, students will be able to

CO 1: Understand the common terms and definitions of virtualization and cloud computing and be able to give examples.

CO 2: Comprehend the technical capabilities and business benefits of virtualization and cloud computing.

CO 3: Describe the landscape of different types of virtualization and understand the different types of clouds.

CO 4: Illustrate how key application features can be delivered more easily on virtual infrastructures.

Unit-1
Teaching Hours:12
Cloud Computing Basics
 

Cloud Computing Basics - cloud computing Overview – Cloud components, Infrastructure, Services - Applications – Storage, Database services - Intranets and the cloud – components, Hypervisor applications - First Movers in the Cloud - Your Organization and Cloud Computing – When you can use Cloud computing, Benefits, Limitations, Security Concerns, Regulatory Issues

Unit-2
Teaching Hours:12
The Business case for going to the Cloud
 

Cloud Computing with the Titans – Google, EMC, NetApp, Microsoft, Amazon,  Salesforce.com, IBM. The Business case for going to the Cloud - Cloud Computing services- Infrastructure as a Service, Platform as a Service, Software as a Service, Software plus services, How applications help your business, Deleting your data center

Unit-3
Teaching Hours:12
Cloud Computing Technology: Hardware and Infrastructure
 

Cloud Computing Technology :Hardware and Infrastructure – Clients – Mobile, thin, Thick - Security - Data leakage, Offloading work, Logging, Forensic, Development, Auditing Network – Basic public Internet, The accelerated Internet, Optimized Internet overlays, Cloud providers, cloud consumers, Services. Accessing the Cloud – Platforms – Web Application framework, Web hosting service, Proprietary methods - Web Applications, Web APIs- What are APIs, How APIs work, API Creators - Web Browsers

 

Unit-4
Teaching Hours:12
Cloud Storage
 

Cloud Storage – Overview-The Basics, storage as a service, Providers, security, Reliability, advantages, cautions, Outages, Theft - Cloud storage providers, Standards- Application – Communication, Security - Client – HTML, Dynamic HTML, JavaScript - Infrastructure – Virtualization, OVF - Service – Data, Web service

Unit-5
Teaching Hours:12
Developing Applications
 

Developing Applications-Google, Microsoft, Intuit Quick Base, Cast Iron cloud, Bungee connect, Development, Trouble shooting, Application Management Local clouds and Thin Clients Virtualization in your Organization - why virtualize, How to virtualize, concerns, security- Server solutions- Microsoft Hyper-V, VMware, VMware Infrastructure, Containers: Using and Managing Containers – Container Basics, Docker and Hub, Container for Science, Creating your own Container, Secure your VMs and Container.

Text Books And Reference Books:

[1] Anthony T Velte, Toby J Velte and Robert Elsenpeter,” Cloud Computing – A Practical Approach”, Tata McGraw Hill Education Pvt Ltd, 2010.

[2] Ian Foster and Dennis B. Gannon, “Cloud Computing for Science and Engineering”, MIT Press, 2017.

 

Essential Reading / Recommended Reading

[1] Syed A. Ahsonand Mohammed Ilyas, “Cloud Computing and Software Services : Theory and Techniques”, CRC Press, Taylor and Francis Group, 2010.

[2] Judith Hurwitz, Robin Bloor, Marcia Kaufman and Fern Halper, “Cloud Computing for Dummies”. Wiley- India edition,2010.

[3] Ronald L. Krutz and Russell Dean Vines, “Cloud Security: A Comprehensive Guide to Secure Cloud Computing”. Wiley Publishing, Inc., 2012.

 

 

Evaluation Pattern

60% CIA + 40% ESE

 

MCSA441A - SOFTWARE PROJECT MANAGEMENT (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

Software Project Management provides insight to the importance of careful project management. Topics are presented in the same order that they appear in the progression of actual projects 

Learning Outcome

Upon completion of the course the student will be able to

CO1: Practice project planning activities that accurately forecast project costs, timelines and quality

CO2: Value software projects effectively

CO3: Assess real world projects with a strong knowledge on basic measurements of monitoring and controlling

Unit-1
Teaching Hours:14
Introduction
 

Introduction to software project management and control whether software projects are different from other types of projects. Scope of project management.Management of project life cycle. Defining effective project objectives where there are multiple stakeholders. Software tools for project management. Project Planning: Creation of a project plan -step by step approach, The analysis of project characteristics in order to select the best general approach, Plan Execution, Scope Management, Use of Software (Microsoft Project) to Assist in Project Planning Activities.

Unit-2
Teaching Hours:12
Project Scheduling
 

Time Management, Project Network Diagram, Critical path Analysis, PERT, Use of Software (Microsoft Project) to Assist in Project Scheduling. Project Cost Management: Resource planning, Cost Estimation (Types, Expert Judgment, Estimation by Analogy, COCOMO).

Unit-3
Teaching Hours:10
Project Quality Management
 

Stages, Quality Planning, Quality Assurance, Quality Control, Quality Standards, Tools and Techniques for Quality Control.

Unit-4
Teaching Hours:12
Project Human Resource Management
 

Definition, Key to managing People, Organization Planning, Issues in Project Staff Acquisition and Team Development, Using Software to Assist in Human Resource Management, Communication Planning, Information Distribution, Performance Reporting.

Unit-5
Teaching Hours:12
Project Risk Management
 

Common Sources of Risk in IT projects, Risk Identification, Risk Quantification, Risk Response Development and Control. Project Procurement Management: Procurement Planning, Solicitation, Source Selection, Contract Administration.

Text Books And Reference Books:

[1] Bob Hughes, Mike Cotterell, “Software Project Management”, Tata McGraw-Hill, 3rd Ed., 2009.

[2] PankajJalote, “Software Project Management in Practice”, Pearson Education, 3rd Ed. , 2010.

[3] Kathy Schwalbe, “Information Technology Project Management”, THOMSON Course Technology, International Student Edition, 2003.

[4] Elaine Marmel, “Microsoft Office Project 2003 Bible”, Wiley Publishing Inc., 2003.

 

Essential Reading / Recommended Reading

[1] Maylor, H.,  “Project Management”, PHI, 3rd Ed., 2002.

[2] Robert T. Futrell, “Quality Software Project Management”, Pearson, 2010.

[3] Bentley C. , “PRINCE2: A Practical Handbook”, NCC Blackwell, 2002.

[4] Robert T. Futrell, “Quality Software Project Management”, Pearson, 2010.

[5] S.A. Kelkar, “Software Project Management - A Concise Study”, PHI, Revised Edition, 2012.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MCSA441B - SOFTWARE ARCHITECTURE (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

 To provide a sound technical exposure to the concepts, principles, methods, and best practices in software architecture and software design.

 

 

Learning Outcome

 

        CO 1: Demonstrate technological aspects of architecture.

 

CO 2: Develop competence in building robust, scalable, and reliable software intensive systems in an extremely.

 

CO 3: Implement appropriate software architecture for any project implementation.

 

 

Unit-1
Teaching Hours:11
Architecture Business Cycle
 

Origin of an Architecture , Software Processes and Architectural Business Cycle, A good architecture, Software Architecture, What is & what it is not the software Architecture is, Other points of view, Architectural Pattern, Reference Models and Reference Architectures, The Importance of Software Architecture, Architectural structures & views, Case study in utilizing Architectural Structures.

Unit-2
Teaching Hours:12
Creating An Architecture
 

Understanding the quality Attributes - Functionality and Architecture, Architecture and Quality Attributes - System Quality Attributes - Quality Attributes Scenarios in practice - Other System Quality Attributes - Business Qualities - Architecture Qualities - Achieving Qualities Introducing Tactics – Availability - Modifiability – Performance – Security – Testability – Usability - Relationships of Tactics to Architectural Patterns - Architectural Patterns and Style

 

Self Study: Achieving Qualities

 

Unit-3
Teaching Hours:11
Design and Documentation
 

Designing the Architecture: Architecture in the life cycle, Designing the Architecture, Forming the Team Structure, Creating the Skeletal System. Documenting Software Architectures, Uses of Architectural Documentation, Views, Choosing the relevant views, Documenting a view, Documentation across views.

Unit-4
Teaching Hours:13
Analyzing Architecture
 

 

ATAM (Architecture Tradeoff Analysis Method) :A comprehensive method for architecture evaluation, participants, outputs, phases of the ATAM, The Nightingale system - A case study in applying the ATAM.CBAM (Cost Benefit Analysis Method): A quantitative approach to architecture design decision making: Decision making context, basis for CBAM, Implementing CBAM.Architecture of ORACLE 12c, Java.

 

Unit-5
Teaching Hours:13
Software Product Lines
 

Reusing Architectural Assets: Overview – Successful working, Scope, Architectures and Difficulties in software product lines. Unix Architecture, Layered & State Diagram, Building systems from off-the-shelf components - Impact of components on Architecture, Architectural mismatch, Component-based design as search, ASEILM example.

 

Text Books And Reference Books:

 

Len Bass, Paul Clements, Rick Kazman, Software Architecture In Practice, Pearson Education Asia , 3rd Edition,2012.

 

Essential Reading / Recommended Reading

[1].  Sommerville and Ian, “Software Engineering”, Addison Wesley, 9th Edition, 2010

[2]. Jeff Garland and Richard Anthony, “Large-Scale Software Architecture –  A  Practical Guide Using UML”, Wiley –Dreamtech India Pvt.,Ltd., 1st Edition,2002.

[3]. Pressman S Roger, “Software Engineering”, McGraw Hill International Editions, 7th Edition,2009.

[4]. Rumbaugh, James, “Object Oriented Modeling and design”, Pearson Education,  New  Delhi,2005.

Evaluation Pattern

   Evaluation Pattern: 60% CIA + 40% ESE

MCSA441C - SOFTWARE QUALITY AND TESTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To understand the need for Software Quality, Tools Used and Metrics involved.

To appreciate software testing principles and methods to detect in the ever changing software technological changes

Learning Outcome

Upon the successful completion of the course the student will be able to;

CO 1: Demonstrate the concepts of Software Quality and Testing.

CO 2: Apply the consepts to Test the codes, artifacts better and different Quality Tools.

CO 3: Understand the advantages of Extreme Testing and High Order Testing.

CO 4: Create effective test plan and test cases.

CO 5: Identify the need for Software Quality Metrics and Assessments.

Unit-1
Teaching Hours:12
Introduction to Software Quality
 

Quality: popular view,professional view, software quality, total quality management, The defect prevention process, process maturity framework and quality standards (CMM , SPR Assessment, Malcolm Bridge, ISO9000).

Unit-2
Teaching Hours:12
Fundamentals in Measurement Theory
 

Levels of measurement: some basic measures, reliability and validity , Software quality  metrics: Product Quality Metrics , in-process quality process , Example of Metrics Program – Motorola, HP.

Unit-3
Teaching Hours:12
Seven Basic Quality Tools
 

Ishikawas’ seven basic tools: checklist, pareto diagram, histogram, runchart, scatter diagram control chart cause and effect diagram.Defect Removal Effectiveness: Literature review, a close look at DRE, defect removal effectiveness and quality planning.

Unit-4
Teaching Hours:12
Fundamentals of Software
 

Software Testing Principles, Economics of Testing Inspection and walkthrough, code inspection, an error checklist for Inspection, Walkthroughs, Desk Checking, Peer Rating Module Testing.

Unit-5
Teaching Hours:12
High Order Testing, Debugging and Extreme Testing
 

High Order Testing - Debugging by Brute Force, Induction, Deduction, Backtracking Extreme Programming basics, Extreme Testing, Extreme Testing Applied.

Text Books And Reference Books:

[1]. Stepen H Kan, Metrics and Models in Software Quality Engineering, 2nd Edition ,reprint 2006.

 

 

 

Essential Reading / Recommended Reading

[1]. Glenford J.Myers , The Art of Software Testing, John Wiley and Sons publications,2004.

Evaluation Pattern

60% CIA + 40% ESE

MCSA441D - OOAD WITH UML (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

Object Oriented Analysis and Design Using UML course provides instruction and practical experience focusing on the effective use of object-oriented technologies and the judicious use of software modeling as applied to a software development process.

Learning Outcome

Upon successful completion of the course the student will be able to

CO 1: Understand the object oriented life cycle, Use-case design, Object Oriented Design process, software quality and usability.

CO 2: Identify objects, relationships, services and attributes through UML.

CO 3: Apply UI design concepts in real-time applications.

Unit-1
Teaching Hours:12
Complexity, The Object Model
 

Complexity: The inherent complexity of software, The Structure of complex systems, Bringing order to chaos, on designing complex systems, Categories of analysis and Design methods. The Object Model: The evolution of object model, Elements of object model, applying the object model, Foundations of the object model.

Unit-2
Teaching Hours:13
Classes and Objects, Classification
 

Classes and Objects: The nature of an object - Relationship among objects, the nature of a class, Relationship among classes - The interplay of classes and objects, On building quality classes and objects, invoking a method - Classification: The importance of proper classification, Identifying classes and objects, Key abstraction and mechanisms, A problem of classification.

Unit-3
Teaching Hours:12
Notation
 

Notation: Basic BehaviouralModelling, Basic elements, class diagram, object, state Transition diagram, Interactions, Use Case Diagrams, Activity, module and process diagrams.

Unit-4
Teaching Hours:10
Process
 

Principles, Micro and macro development process, Pragmatics- Management and planning, staffing, Release management, Reuse, Quality Assurance Metrics, Documentation, Tools, The benefits and risks and Object-oriented development.

Unit-5
Teaching Hours:13
Case Study:
 

A domain based analysis and design using rational rose can be made

Unit-5
Teaching Hours:13
Architectural Modelling
 

Components, Deployment, Collaborations, Pattern and Frameworks, Component Diagram, Deployment Diagrams, Systems and Models

Text Books And Reference Books:

 [1] Grady Booch, “Object-Oriented Analysis and Design With Applications”, Pearson Education, 3rd edition, 2009.

Essential Reading / Recommended Reading

 [1] ​Mahesh P. Matha, “Object-Oriented analysis and Design Using UML”, PHI, 3rd reprint, 2012.

Evaluation Pattern

CIA: 60%

ESE: 40%

MCSA441E - PRINCIPLES OF USER INTERFACE DESIGN (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

The objective of this course: is for students to learn how to design, prototype and evaluate user interfaces to effective browse and search systems by examining what research has uncovered, what developers have produced, and how people perform information tasks.

Learning Outcome

Upon the successful completion of  the course, student will be able to

CO1: Demonstrate main concepts in human computer interaction.

CO2: Understand the basic user interface principles, cognitive and perceptual abilities and constraints that impact information use.

CO3: Apply human information processing to the design of user interfaces.

CO4: Design and prototype user interfaces. 

CO5: Identify research issues in user interface design.

Unit-1
Teaching Hours:12
Goals of User-Interface Design
 

Human factors in user interface design, Theories, Principles, and Guidelines, Goals of Systems Engineering, Accommodation of Human Diversity, Goals for Our Profession, High Level Theories, Object-Action Interface model, Principle 1:Recognize the Diversity, Principle 2: Use the Eight Golden Rules of Interface Design, Principle 3: Prevent Errors, Guidelines for Data Display, Guidelines for Data Entry, Balance of automation and Human Control, Practitioner’s Summary, Researcher’s Agenda. 

Management Issues - Introduction, Organizational; Design to Support Usability, The three Pillars of Design, Development Methodologies, Ethnographic  Observation, Participatory Design, Scenario Development, Social Impact Statement for Early Design Review, Legal issues, Expert Reviews, Usability, testing and Laboratories, Surveys, Acceptance tests, Evaluation During Active Use, Controlled Psychologically Oriented Experiments, Practitioner’s Summary, Researcher’s agenda. 

Unit-2
Teaching Hours:12
Tools Environment, and Menus
 

Introduction, Specification Methods; Interface-Building Tools, Evaluation and critiquing Tools. Direct Manipulation and virtual Environments: Introduction, Examples of Direct manipulation systems, Explanations of Direct manipulation, Visual Thinking and Icons, Direct Manipulation Programming, Home Automation, Remote Direct manipulation, Virtual Environments Menus: Task-Related Organization, Item Presentation Sequence, Response Time and Display Rate.

Fasty Movement through Menus, Menu Layout, Form Filling, Dialog boxes, Command-Organization strategies, The Benefits of Structure, Naming and Abbreviations, Command Menus, Natural Language in Computing, Practitioners Summary, Researcher’s Agenda. 

Unit-3
Teaching Hours:12
Interaction Devices
 

Response Times, Styles and Manuals: Interaction Devices, Introduction, Keyboards and Function Keys, Pointing Devices, speech Recognition, Digitization, and Generation, Image and Video displays, Printers. Response Time and Display Rate: Theoretical; Foundations, Exceptions and attitudes, User Productivity, variability, Presentation Styles and Manuals: Introduction, Error messages, Nonanthopomorphic Design, Color of Manuals, Help: Reading From paper Versus from Displays, Preparation of Printed manuals,  Preparation of Online Facilities, Practitioner’s Summary, Researcher’s Agend.

Unit-4
Teaching Hours:12
Multiple-Windows
 

Computer-Supported Cooperative work, Information’s search and www  Multiple-Windows Strategies: Introduction, Individual-Window Design, Multiple-window Design, Coordination by Tightly-Coupled Windows, Image Browsing and Tightly-Coupled Windows, Personal Role Management and Elastic Windows Computer-Supported Cooperative Work; Introduction, Goals of Cooperation, Asynchronous Interactions: Different Time, Different Place, Synchronous Distributed: Different Place, Same Time, Face to Face: Same Place, Same Time, Applying CSCW to Education.

Unit-5
Teaching Hours:12
Information Search and Visualization
 

Introduction, Database Query And Phrase Search in Textual Documents, Multimedia Document Searches, Information Visualization, Advanced Filtering. Hypermedia and the World wide Web: Introduction, Hypertext and Hypermedia, World Wide Web, Genres and Goals and Designers, Users and Their Tasks, Object Action Interface Model for Web Site Design, Practitioner’s summary, Researcher’s Agenda.

Text Books And Reference Books:

[1] Ben Shneiderman, Designing the User Interface, Pearson Education, 5th Edition, 2010

[2] Wilber O Galitz, An Introduction to GUI Design Principles and Techniques, John- Wiley &Sons, 2007.

Essential Reading / Recommended Reading

[1] Jeff Johnson, Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Rules , Morgan Kaufmann, 1st Edition, 2010.

[2] Alan Dix, Human-Computer Interaction, Pearson,2009.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MCSA441F - RISK ANALYSIS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

To provide fundamental concepts of risk analysis and relevant details related to types of risks, risk management strategies and tools used for risk analysis.

Learning Outcome

Upon successful completion of the course, the student will be able to

CO 1: Identify the different risks involved in finance arena.

CO 2: Analyze the legal issues affecting the business.

CO 3: Categorize the various risks faced by an organization.

CO 4: Explore the tools and practices needed to assess and evaluate financial risks.

Unit-1
Teaching Hours:12
Introduction to Risk Analysis
 

Definition -Understanding Risk- Nature of Risk, Source of Risk, Need for risk, management, Benefits of Risk Management, Risk Management approaches.

Unit-2
Teaching Hours:12
Risk Classification
 

Credit risk, market risk, operational risk and other risk, Risk Measurements -Measurement of Risk – credit risk measurement, market risk, measurement, interest rate risk measurement, Asset liability management, measurement of operational risk

Unit-3
Teaching Hours:12
Risk Management
 

Risk management- Managing credit risk, managing operational risk, managing market risk, insurance

Unit-4
Teaching Hours:12
Tools for Risk Management
 

Derivatives, combinations of derivative instruments, Neutral and volatile strategies, credit derivatives, credit ratings, swaps.

Unit-5
Teaching Hours:12
Regulation and Other Issues
 

Issues in risk management – Regulatory framework, Basel committee, legal issues, accounting issues, tax issues, MIS and reporting, integrated risk management

Text Books And Reference Books:

[1].    Dun, Bradstreet, “Financial Risk Management”, TMH, 2006.

Essential Reading / Recommended Reading

[1]. John C Hull, “Risk management and Financial Institutions”, Pearson, 2015.

[2]. AswathDamodharan,“Strategic Risk Taking”, Pearson, 2008.

Evaluation Pattern

60% CIA + 40% ESE

MCSA442A - MACHINE LEARNING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To acquire basic knowledge in machine learning techniques and learn to apply the techniques in the area of pattern recognition and data analytics

Learning Outcome

CO 1: Understand the basic principles of machine learning techniques.

CO 2: Demonstrate supervised and unsupervised machine learning algorithms.

CO 3: Apply appropriate techniques for real time problems.

Unit-1
Teaching Hours:12
Introduction
 

Machine Learning - types of machine learning examples - Supervised Learning: Learning class from examples - VC dimension - PAC learning – noise - learning multiple classes – regression - model selection and generalization - dimensions of a supervised learning algorithm - Parametric Methods: Introduction - maximum 

Unit-2
Teaching Hours:12
Dimensionality Reduction
 

Introduction - subset selection - principal component analysis - factor analysis - multidimensional scaling - linear discriminant analysis - Clustering: Introduction - mixture densities - k-means clustering - expectation-maximization algorithm - hierarchical clustering - choosing the number of clusters - Non-parametric: Introduction - non-parametric density estimation - non-parametricclassification.

Unit-3
Teaching Hours:12
Decision Trees
 

Introduction, univariate trees, pruning, rule extraction from trees, learning rules from data. Multilayer perceptron: Introduction, training a perceptron, learning Boolean functions,multilayer perceptron, backpropogation algorithm, trainingprocedures.

Unit-4
Teaching Hours:12
Kernel Machines
 

Introduction, optical separating hyperplane, v-SVM, kernel tricks, vertical kernel, defining kernel, multiclass kernel machines, one-class kernel machines. Bayesian Estimation:


Introduction, estimating the parameter of a distribution, Bayesian estimation, Gaussian processes. Hidden Markov Models: Introduction, discrete Markov processes, hidden Markov models, basic problems of HMM, evaluation problem, finding the state sequence, learning model parameters, continuous observations, HMM with inputs, model selection withHMM.

Unit-5
Teaching Hours:12
Graphical Models
 

 

Introduction, canonical cases for conditional independence,d-separation, Belief propagation, undirected graph: Markov random field. Reinforcement Learning: Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partially observed state.

Self Learning: Clustering - Decision tree

Service Learning: Introduction to machine learning applications developed for betterment of society through select case studies.

Text Books And Reference Books:

[1] E. Alpaydin, “Introduction to Machine Learning”. 2nded, MIT Press, 2009.

Essential Reading / Recommended Reading

[1]. K. P. Murphy, “Machine Learning: A Probabilistic Perspective”,. MIT Press, 2012. [2]. P. Harrington, “Machine Learning in Action”, Manning Publications, 2012

[3]. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2011.

[4]. S. Marsland, “Machine Learning: An Algorithmic Perspective”, 1st  Ed.  Chapman  and Hall,2009.

[5]. T. Mitchell, “Machine Learning”, McGraw-Hill, 1997

 

Evaluation Pattern

CIA 60%

ESE 40%

MCSA442B - NEURAL NETWORKS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Understand the concept of Neural Networks, models of artificial neural networks and its applications.

Learning Outcome

Upon successful completion of the course, the student will be able to

      CO 1: Understand the concepts of neural networks.

CO 2: Understand the concepts of feedforward and backward neural networks.

CO 3: Design basic neural networks

CO 4: Implement neural networks concepts as solutions to real-time problems

Unit-1
Teaching Hours:11
Introduction
 

Fundamental concepts and Model: Biological Neurons and their Artificial models, Models of artificial Neural Networks, Neural processing, Learning and Adaptation, Neural network Learning rules- Hebbianrule, Perceptron rule, Delta rule

Unit-2
Teaching Hours:12
Single layer Perceptron Model
 

Single-layer perceptron classifiers: Classification model, Features and decision regions, Discriminant functions, Linear machine and Minimum distance classification, Non-parametric training concept, Training and Classification using the Discrete perceptron: algorithm and example, Single layer continuous Perceptron networks for linearly separable classifications.

Unit-3
Teaching Hours:12
Multi Layer Feed Forward Networks
 

Multilayer feed forward Networks: Linearly separable Pattern classification, Delta learning rule for Multiperceptron model, Generalized Delta learning rule, Feed forward recall and error back propagation training.

Unit-4
Teaching Hours:13
Single Layer Feedback Networks
 

Single-layer Feedback Networks: Basic concepts of dynamic systems, Mathematical foundations of Discrete-time Hopfield Networks, Mathematical foundations of Gradient type Hopfield networks, Associative memories: Basic concepts, Linear Associator.

Unit-5
Teaching Hours:12
Associative Memory
 

Bidirectional associative memory - associative memory for spatio-temporal patterns - Case study: Implementation of NN in anysimulator

Self Learning: Bidirectional Associative memory

Text Books And Reference Books:

Jacek M. Zurada, “Introduction to Artificial Neural networks”Jaico Publishing,2006.

Essential Reading / Recommended Reading

 

 [1]. Limin Fu,“Neural Network in Computer Intelligence”,TMH,1994.

[2]. Yegnanarayana, “Artificial Neural Networks”,PHI Learning,2007.

 

Evaluation Pattern

CIA: 60%

ESE: 40%

MCSA442C - DIGITAL IMAGE PROCESSING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The objective of this course is to cover the basic theory and algorithms that are widely used in Digital image processing. Develop hands-on experience in using computers to process images with MATLAB image processing toolbox.

Learning Outcome

Upon successful completion of the course the student would

CO 1: Understand the theoretical background of Image processing.

CO 2: Apply image enhancement, restoration, compression and segmentation in both frequency and spatial domain.

CO 3: Represent and recognize objects through patterns in application.

Unit-1
Teaching Hours:12
Introduction and Digital Image Fundamentals
 

The origins of Digital Image Processing - Fundamental Steps in Image Processing - Elements of Digital Image Processing System - Image Sampling and Quantization - Basic relationships: Neighbors – Connectivity - Distance Measures between pixels - Linear and Non Linear Operations

Unit-2
Teaching Hours:12
Image Enhancement in Spatial Domain
 

Gray Level Transformations - Histogram Processing - Histogram equalization - Histogram specification - Basics of Spatial Filters - Smoothening and Sharpening Spatial Filters - Image Enhancement in Frequency Domain - Introduction to Fourier Transform and the frequency Domain - Smoothing and Sharpening - Frequency Domain Filters.

Self Learning: Homomorphic Filtering

Unit-3
Teaching Hours:12
Image Restoration and Image Compression
 

A model of The Image Degradation / Restoration Process - Noise Models - Restoration in the presence of Noise - Periodic Noise Reduction by Frequency Domain Filtering - Image Compression models - Huffman coding - Run length coding - LZW coding

Unit-4
Teaching Hours:12
Image Segmentation and Representation
 

Point, Line and Edge detection - Thresholding – Basic global thresholding - optimum global thresholding using Otsu’s Method - Region Based Segmentation - Region Growing and Region Splitting and Merging - Representation - Chain codes

Self Learning : Polygonal approximations using minimum perimeter polygons.

Unit-5
Teaching Hours:12
Description and Object Recognition
 

Boundary descriptors - Fourier descriptors - Regional descriptors - Topological descriptors and Moment invariants - Introduction to Patterns and Pattern Classes – Decision - Theoretic Methods – Minimum distance classifier - KNN classifier and Bayes

Self Learning: classifier

Text Books And Reference Books:

[1]. R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, 3rd Edition. Pearson Education, 2009.

[2]. A.K. Jain, “Fundamental of Digital Image Processing”, 4th Edition.PHI, 2011.

[3].    Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, “Digital Image Processing Using MATLAB”, 2nd Edition. PHI, 2009.

Essential Reading / Recommended Reading

[1].   M.  A.  Joshi,  “Digital  Image  Processing:  An  algorithmic approach”, 2nd Edition.     PHI, 2009.

[2]. B.Chanda and D. Dutta Majumdar, “Digital Image Processing and analysis”, 1st Edition, PHI, 2011.

Evaluation Pattern

CIA: 60%

ESE: 40%

MCSA442D - COMPUTER VISION (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The goal of computer vision is to develop the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analyzed from a single image or a set of images. This course will cover the fundamentals of ComputerVision.

Learning Outcome

On completion of the course students will be able to

CO 1: Understand the Concepts of Computer Vision , Image Formation and Representation

CO 2: Identify different image processing methods like Image Filtering (spatial domain), Mask-based (e.g., correlation, convolution), Smoothing (e.g., Gaussian), Sharpening (e.g., gradient), Edge Detection (e.g., Canny, Laplacian of Gaussian), Interest Point Detection (e.g., Moravec, Harris), Shape representation and Segmentation

CO 3: Implement appropriate approach in real-time applications.

CO 4: Design tools to process real-time graphic data for research.

Unit-1
Teaching Hours:14
Image Formation Models
 

Monocular Imaging System - Orthographic & Perspective Projection - Camera model and Camera calibration - Binocular imaging systems

Image Processing and Feature Extraction, Image representations (continuous and discrete) - Edge detection

Unit-2
Teaching Hours:10
Motion Estimation
 

Regularization theory - Optical computation - Stereo Vision - Motion estimation - Structure from motion

Unit-3
Teaching Hours:12
Shape Representation and Segmentation
 

Deformable curves and surfaces - Snakes and active contours - Level set representations - Fourier and wavelet descriptors - Medial representations, - Multi-resolutionanalysis.

Unit-4
Teaching Hours:12
Object recognition
 

Hough transforms and other simple object recognition methods - Shape correspondence and shape matching - Principal Component analysis - Shape priors for recognition.

Unit-5
Teaching Hours:12
Applications
 

Application - finding in digital Libraries - organizing collections of images - including what do users want - how well does the system work - Representations of parts of the picture - including segmentation - template matching - shape and correspondence - clustering and organizing collections - searching and browsing - Images based rendering - Constructing 3D models from image sequences - including scene modeling from registered and unregistered images.

Text Books And Reference Books:

[1]       Forsyth. Ponce, “Computer Vision – A Modern approach” , 2ndEdition, Pearson Education, 2003.

Essential Reading / Recommended Reading

[1] Milan Sonka, Vaclav Hlavac and Roger Boyle, “Digital Image Processing and Computer Vision”, Thomson South-Western, Canada, 2008.

[2] Richard Szeliski, “Computer Vision and Applications”, New Age Internations (P) Ltd.,  New Delhi,2005.

[3] S Nagabhushana, "Computer Vision and Image Processing", New Age Internations (P) Ltd., New Delhi, 2005.

Evaluation Pattern

CIA: 60%

ESE: 40%

 

 

 

MCSA442E - AGENT BASED COMPUTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

On completion of the course students should have understood Software Agents and its Applications, Intelligent learning Methods.

Learning Outcome

On completion of the course students will be able to 

CO1: Understand Software Agents and its Applications

CO2: Apply Intelligent learning Methods

CO3: Analyze Rule learning with case studies

Unit-1
Teaching Hours:12
SOFTWARE AGENTS
 

Introduction to Software Agents: What is a software agent? - Why software agents? - Applications of Intelligent software agents-Practical design of intelligent agent systems.

Unit-2
Teaching Hours:12
INTELLIGENT AGENTS
 

Intelligent Agent Learning- Approaches to Knowledge base development-Disciple approach for building Intelligent agents- Knowledge representation-Generalization- Problem solving methods-Knowledge elicitation.

Unit-3
Teaching Hours:12
RULE LEARNING
 

Rule learning problem- Rule learning method- Learned rule characterization. Rule refinement: Rule refinement problem- Rule refinement method- Rule experimentation and verification-Refined rule characterization-Agent interactions.

Unit-4
Teaching Hours:12
BUILDING INTELLIGENT AGENTS
 

Disciple shell: Architecture of Disciple shell- Methodology for building Intelligent Agents- Expert-Agent interactions during knowledge elicitation process- Expert-Agent interactions during rule learning process- Expert-Agent interactions during rule refinement process.

Unit-5
Teaching Hours:12
CASE STUDIES
 

Case studies in building intelligent agents: Intelligent Agents in portfolio management- Intelligent Agents in financial services- Statistical Analysis assessment and support agent- Design assistant for configuring computer systems.

Text Books And Reference Books:

[1] Nicholas R Jennings, Michael J Wooldridge (Eds.), “Agent Technology – Foundations, Applications and Markets”, Springer, 1997.

Essential Reading / Recommended Reading

[1] Jeffrey M Bradshaw, “Software Agents”, AAAI Press/ the MIT Press, Standard Edition, 1997.
[2] Gheorghe Tecuci et al., “Building Intelligent Agents”, Academic Press, 2003.
[3] Eduardo Alanso, Daniel Kudenko, Dimitar Kazakov (Eds.) “Adaptive Agents and Multi-Agent Systems”, Springer Publications, 2003.

Evaluation Pattern

CIA Weight

ESE Weight

60%

40%

MCSA442F - EVOLUTIONARY COMPUTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Evolutionary Computing (EC) is a field of nature inspired computing that are mainly used for solving optimization problems. Major Evolutionary Algorithms (EAs) are Genetic Algorithms, Evolution Strategies, Genetic Programming, Differential Evolution, and Learning Classifier Systems. Applications of EA include evolution of engineering design, controls systems, automatic programming and many more. In this course, students will learn the basics of EA and understand the applications using online simulation tools. This course will give the concepts of evolutionary computing techniques and popular evolutionary algorithms that are used in solving optimization problems. Students will be able to implement custom solutions for real-time problems applicable with evolutionary computing.

Learning Outcome

  • Basic understanding of evolutionary computing concepts and techniques
  • Classify relevant real-time problems for the applications of evolutionary algorithms
  • Design solutions using evolutionary algorithms

Unit-1
Teaching Hours:12
Introduction to Evolutionary Computing
 

Terminologies – Notations – Problems to be solved – Optimization – Modeling – Simulation – Search problems – Optimization constraints - Gentic Algorithms: History of genetics – Science of genetics – History of genetic algorithm – Simple binary genetic algorithm – continuous geneticalgorithm

Unit-2
Teaching Hours:12
Evolutionary Programming
 

Continuous evolutionary programming – Finite state machine optimization – Discrete evolutionary programming – The Prisoner’s dilemma - Evolution Strategy: One plus one evolution strategy – The 1/5 Rule – (μ+1) evolution strategy – Self adaptive evolution strategy

Unit-3
Teaching Hours:12
Genetic Programming
 

Fundamentals of genetic programming – Genetic programming for minimal time control - Evolutionary Algorithm Variation: Initialization – Convergence – Population diversity – Selection option – Recombination – Mutation

Unit-4
Teaching Hours:12
Ant Colony Optimization
 

Pheromone models – Ant system – Continuous Optimization – Other Ant System - Particle Swarm Optimization: Velocity limiting – Inertia weighting – Global Velocity updates – Fully informed Particle Swarm

Unit-5
Teaching Hours:12
Multi-Objective Optimization
 

Pareto Optimality – Hyper volume – Relative coverage – Non-pareto based EAs – Pareto based EAs – Multi-objective Biogeography based optimization

Text Books And Reference Books:

[1] D. Simon, Evolutionary optimization algorithms: biologically inspired and population- based approaches to 

     computer intelligence. New Jersey: John Wiley,2013.

Essential Reading / Recommended Reading

[1]           Eiben and J. Smith, Introduction to evolutionary computing. 2nd ed. Berlin: Springer,2015.

[2]           D. Goldberg, Genetic algorithms in search, optimization, and machine learning. Boston: Addison-Wesley,2012.

[3]           K. Deb, Multi-objective optimization using evolutionary algorithms. Chichester: John Wiley & Sons, 2009.

[4]           R. Poli, W. Langdon, N. McPhee and J. Koza, A field guide to genetic programming. [S.l.]: Lulu Press,2008.

[5]           T. Bäck, Evolutionary algorithms in theory and practice. New York: Oxford Univ. Press, 1996.

 

Web Resources:

  1. E. A.E and S. J.E, "Introduction to Evolutionary Computing | The on-line accompaniment to the book Introduction to Evolutionary Computing", Evolutionarycomputation.org, 2015. [Online]. Available: http://www.evolutionarycomputation.org/. [Accessed: 24- Jan-2020].
  2. F. Lobo, "Evolutionary Computation 2018/2019", Fernandolobo.info, 2018. [Online]. Available: http://www.fernandolobo.info/ec1819/. [Accessed: 24- Jan-2020].
  3.  "EC lab Tools", Cs.gmu.edu, 2008. [Online]. Available: https://cs.gmu.edu/~eclab/tools.html. [Accessed: 24- Jan- 2020].
  4.  "Kanpur Genetic Algorithms Laboratory", Iitk.ac.in, 2008. [Online]. Available: https://www.iitk.ac.in/kangal/codes.shtml. [Accessed: 24- Jan-2020].
  5. "Course webpage Evolutionary Algorithms", Liacs.leidenuniv.nl, 2017. [Online]. Available: http://liacs.leidenuniv.nl/~csnaco/EA/misc/ga_demo.htm. [Accessed: 24- Jan-2020].
Evaluation Pattern

CIA: 60%

ESE: 40%

MCSA481 - MAIN PROJECT (2019 Batch)

Total Teaching Hours for Semester:20
No of Lecture Hours/Week:2
Max Marks:200
Credits:4

Course Objectives/Course Description

 

     

Learning Outcome

   

Unit-1
Teaching Hours:20
Project
 

It is a full time project to be taken up either in the industry or in an R&D organization.

Text Books And Reference Books:

 

 

 

 

Essential Reading / Recommended Reading

 

 

Evaluation Pattern

  -

MCSA482 - RESEARCH (IMPLEMENTATION AND PUBLICATION) (2019 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

Research in Computer Science : Research inclusive curriculum is an initiative by the Department of Computer Science through which regular curriculum of Post Graduate Computer Science courses is augmented with formal research.

(a) Inculcating research culture among the post graduate students

(b) Enhancing employability skills of students by providing necessary research foundation

Learning Outcome

Upon the completion of the course the student will be able to

CO1: Demonstrate their understanding of Research article publication process – correcting review comments.

CO2: Able to produce commercially valuable intellectual property. 

CO3: Able to produce new products/processes/methods/model/Framework.

Unit-1
Teaching Hours:30
Research Implementation and publication
 

Research work carried out in this semester is divided in two parts.

1. Modelling and implementation of research work. Students should perform the following tasks:

 ·         Implementation of proposed Methodology

 ·         Evaluation and Discussion of Results

 ·         Limitations, Conclusions and Scope for future enhancements

 ·         Plagiarism report

2. Publications

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

There is only CIA for this paper. Research work carried out in this semester is divided in two parts.

1. Modelling and implementation of their research work. Students should perform the following tasks:

 ·         Methodology

 ·         Evaluation and Discussion of Results

 ·         Limitations, Conclusions and Scope for future enhancements

 ·         Plagiarism report

 2. Publications

Evaluation rubric for Presentation :

Evaluation Rubrics for Research Publication (Weightage – 30 Marks)

S.No

Type of publication

Range of marks

1

National Journal

16 – 20

2

International Journal

21 – 25

3

Scopus/SCI Journal

Above 25